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Large language models have exhibited robust performance across diversenatural language processing tasks. This report introduces TechGPT-2.0, aproject designed to enhance the capabilities of large language modelsspecifically in knowledge graph construction tasks, including named entityrecognition (NER) and relationship triple extraction (RTE) tasks in NLPapplications. Additionally, it serves as a LLM accessible for research withinthe Chinese open-source model community. We offer two 7B large language modelweights and a QLoRA weight specialized for processing lengthy texts.Notably,TechGPT-2.0 is trained on Huawei's Ascend server. Inheriting allfunctionalities from TechGPT-1.0, it exhibits robust text processingcapabilities, particularly in the domains of medicine and law. Furthermore, weintroduce new capabilities to the model, enabling it to process texts invarious domains such as geographical areas, transportation, organizations,literary works, biology, natural sciences, astronomical objects, andarchitecture. These enhancements also fortified the model's adeptness inhandling hallucinations, unanswerable queries, and lengthy texts. This reportprovides a comprehensive and detailed introduction to the full fine-tuningprocess on Huawei's Ascend servers, encompassing experiences in Ascend serverdebugging, instruction fine-tuning data processing, and model training. Ourcode is available at https://github.com/neukg/TechGPT-2.0

TechGPT-2.0: A large language model project to solve the task of knowledge graph construction